A monitoring platform for the built environment: towards the development of an early warning system in a seismic context

The aim of this paper is to present a monitoring system for the built environment based on electrical impedance sensors, together with the development of an early warning system to support decision-making processes in a seismic context. In particular, preliminary data were collected on mortar specimens embedding stainless-steel electrodes for the periodic measurement of electrical impedance. Hence, these data were exploited to train a Neural Prophet-based deep learning model for the prediction of the electrical impedance module. Indeed, this quantity can provide a lot of information about the health status of the monitored structures. The results can be exploited for the development of an early warning system supporting decision-making strategies for the building management. The model can predict the trend of electrical impedance with acceptable accuracy (MAPE <2%); hence, the monitoring platform can provide information suitable for the development of an early warning system.

Automated summary

The paper introduces a monitoring system for the built environment using electrical impedance sensors and develops an early warning system for decision-making in a seismic context. Preliminary data on mortar specimens were collected to predict the electrical impedance module using a Neural Prophet-based deep learning model. The results can be used to support decision-making strategies for building management with acceptable accuracy.